A Method to Distinguish Safe from Less Safe Driving

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A METHOD TO DISTINGUISH SAFE FROM LESS SAFE DRIVING Armin S.

Janitzki

FB 14, Nachricht ent echnik , Universitiit -Gesamthoch schule Paderborn , Pohlweg 47 -49 , 4790 Paderborn , Fed eral R epublic of Germany

Abstrctct_ Recording biological parameters of the driver, parameters of the car, and parameters of the environment it i s pos sible to calculate the difference between normal safe and less normal les s sa fe dri vinQ _ This could be proved in case of driving in a tired state with the parameters pul se rate, skin resistance, and s teering wheel rever sal rate _ A method which allows to calculate a normal pattern of drivinQ as a comparing criterion for actual dri vinQ co ndition i s proposed us ing inducti ve entropy and indu ctive informati on_ Keyword s . Man-machine systems; automobiles; human factor s ; learning sys tems; biocybernetics; safety. INTRODUCTION Statistics say that more than 80% of accidents on roads are caused by the factor human failure in contrast to machine failure (Dil1ing, 19 77; HUK-Verband, 1975, 19 77; ADAC). Regarding the intersection between the man and the machine, a machine normally can do its work time-invariant, whereas human beings differ strongl y wher. doing their wor k at one time or at another (see Fig. 1. ) .

As long as the capacity of men exc eeds the requirements of a task in cooperation with a machine, e.g. safe driving, no problems result. At a time, when a man can't fu1fi1 1 his part of the task any longer the machine generally can not increase proportionally its part and therefore the probability of failu re is raised. If it were pos s ible to mea sure the time variant ability of men a driving task could be done at a time, when a good state of the man guarantees a secure cooperati on.

work A very important s ize at the intersecti on man-machine is the informati on flow and especially the information processing of me n.

man

machine Most steps to improve the information flow of drivers were done collectively for all drivers, for example by better streets, better traffic signs, better driving schools and safety campaigns or by traffic guidance systems (Br~gas, 1974; Busch, 1968 ; Tejmar, 1979; Vogt 1977, Haftkopf, 1977; Kumm, 1970, 19 74; Tafe1, 1978 ) .

t Fig.l . Men 's and machine's work as a function of time

r.

1.

r.S .

-H

2 15

A.S . Janitzki

2 16

Here a method is proposed which depends on the very individual situation of an individual driver and which takes into account the time-variant data processing of men (Janitzki, 1982) .

coordinated activity

INFORMATION PROCESSING A driver takes informations from his environment and he himself reacts by giving informations to his environment. Therefore the information flow i s a very important process when driving a car. The quality of the driving process depends on the quality of the driver's information flow, especially on the driver' s ability to receive informations, on the transmission of informations, and on the space of time between an event and the reaction to this event. Most informations which reach the central nervous system come from the eyes. Only about loo bit/s out of more than 10 6 bit /s of the optical information can be processed consciously. Therefore only important data must remain after incoming informations have been filtered. This means a very effective reduction of information (Kupfmuller, 1959; Keidel, 1964; Grusser, 1972; Spreng, 1976). Interpreting now the law of Yerkes-Dodson (Radl, 1976) we can see in Fig. 2. that too many informations per time lower the coordinated activity of men because important data can no longer be separated from unimportant ones by filtering. An unorganized behaviour is the result. On the other hand a too low information flow does not reach a threshold value which is necessary for data processing in the central nervous system and the activity is lowered, too. Between these two sections an optimum of coordinated activity leads here to an optimum performace of a driving task. Measuring the information flow it could be possible to decide whether a driver currently acts with his optimum activity or whether he does not.

information flow bit /s Fig. 2. Coordinated activity as a functi on of informati on flow There are two methods which allow an estimation of information processi ng . First there are psychologi ca l measurements, e.g. solution of secondary tasks. They are here excluded because they can't be continuously recorded as process parameters and furthermore because an interference with the primary task usually can't be avoided. Secondly, in contrast to the psychological measurements, the physiological measurements don't or nearly don't influence human behaviour and can be recorded continuously. All the measurements should neither restrict nor handicap the usual driving behaviour, e.g. by wires of electrodes to the data processing equipment, by spectacles or invas ive method s, etc . . Two biological parameters were selected, the skin resistance and the heart frequency (Leva, 191 3; Heinze, 1955; Michael s, 1960; Overhof, 1960; Montagu, 1963; Greenshield s , 1963; Hoffmann, 1966; Venables, 1967; Helander, 1975) . Combining these parameters with vehicle data, the steering wheel reversal rate (Sewell, 1975 ), one can estimate the driver's reactions to informations. If it is assumed that a car driver normally take s care of the rules of the road traffic and that he very seldom breaks those rules, it can be concluded that also a less safe driving results when his behaviour s trongly differs from the normal. Assuming further that good coordinated activity corresponds to a normal information flow and a normal and safe driving behaviour we have calculated the inductive

Di s tin guishin g Saf e from Less Safe Dri v in g

entropy for individual drivers in cases of normal driving and when the driver was tired. DRIVING PATTERN Using Carnap's formula (Carnap, 1959) of inducti ve probabi I i ty Px,n n

Px,n =

x

+

Cl

(1 )

n + (). z

where nx is the number of events of tne class x after n observations and Cl is a learning coefficient which defines the interval of confidence of a first estimation, and z is the number of different events, an inductive information Ix,n and an inductive entropy Hn can be defined: I

x,n

1d

x1: Px,n . I x,n

(2) (3)

These formulae are successively usable for limited amounts of events. The better known a system is, the lower information Ix,n and entropy Hn a re. Events wh i ch occur very se 1dom give a lot of information. Therefore information and entropy increase. Events which are predictable with a high probalitiy do not increase information and entropy (Marko, 1965). If we regard the deviation of the information dI x,n /dt and of the entropy dH n /dt negative values indicate a better prediction and a lower surprise concerning a special event x or the whole system, respectively. To detect a minimum of surprise the second deviation d2 I x,n / dt 2 can be used. A minimum of surprise then corresponds to a maximum of safety because all events can be predicted best and accidents can be avoided best. We have now got a system which can statistically learn what a pattern looks like and in our case what a driver's normal behaviour looks like. In general, Eqs. (1) to (3) are time dependent for a continuous observation. To reduce

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the amount of data we sampled al I parameters every minute. Then all deviations of Eqs. ( 1) to ( 3 ) can be written with differences between sampled values . As it is not imp ortant at which distinct time an increase in information happened, it is also unimportant to know the variation of entropy in the past. This means that a learning process is time independent. For example it is unimportant whether a learner-driver learns his lessons in the morning or in the evening. Therefore we can take an indication vector as a driving pattern from only the last valuses containing all learned lessons. To see whether an actual behaviour differs from this pattern, it is compared with actual time dependent values . MEASUREMENTS All parameters were recorded analogously by means of a tape-recorder within the car. In the laboratory all data were processed after analog-digital conversion by the aid of a digital computer (Janitzki, 1982). The motion of the steering wheel was recorded by a ten-turn potentiometer which was driven from the steering column by a belt within a Ford Taunus estate car. Heart frequency was taken p1etysmographica11y from a finger by an optical sensor. Skin resistance measurements were performed by two gold electrodes placed on the left forearm. The dc-current was kept at a constant current of l ~ A to guarantee an Ohmic behaviour of the skin resistance. Only differences 6R of the resistance were recorded because absolute values of the skin resistance differ very much from person to person and even the same person shows time dependent values of this parameter. For normal driving and driving when tired the inductive information and inductive entropy were stored for both cases. The system exclusively learned these two situations for several driving hours of the same driver.

A.S . Janitzki

:.' 18

Table 1 shows the results of a driver A under normal and under tired drivinl) condition. TABL E 1 Va lues of Hn and I x,n for Normal and Tired Drivinl)

driver A

normal

Hn I 1, n I 2,n I 3,n Hn I 1, n I 2,n I 3,n

0.89 l. 69 0.54 0.95

bit bit bit bit

ti red l. 42 bit

2.82 bit 0.95 bit l. 58 bit

- entropy of sys tern

- informati on from pulse rate - information from steering reversal rate - information from skin resistance

It can be seen that when a driver is tired, the time independent inductive entropy as wel l as the inductive informations are much higher than usually. In Fig. 3. the entropy Hn as a function of time of a tired driver can be compared with the entropy level of a normal driving behavi our. • H I bit I I

n

normal the style of driving is. This is a tendency to less safe driving. Mainly events which can be defined by means of a corre lat ion method between two parame ters, for example between 11 ,n and 12 ,n ' bring a good selection of important data. A high ly dangerou s s ituation corresponds e.g. to a high pulse rate and a low skin resistance . The skin resistance reacts very fast on an event within only a few seconds whereas the maximum of the heart frequency is reached normally about 30 to 50 seconds later. Therefore an event can be defined if a decrease of skin resistance is fol lowed by an increase of heart frequency. These situations are normally very rare and therefore i f they appear a great entropy increase indicates the stress s itua tion of the driver. In practice differences between entropy va lues are much smaller than in Table 1 because s ituati ons of normal and less safe driving alternate. To obtain more information on the process it is useful to regard the inductive information I x,n where deviations from mean values are easier to be seen. Figure 4 shows these values plotted for the first minutes with a tired driver .

l.6 l.2

4

0.8

3

t

I x, nIbi t

driver A

driver A

0.4 o o

2

4

6

8

10 12 14

16 18 t/mi n

Fig. 3. Inductive entropy Hn as a function of time; tired driver After a few minutes the entropy Hn is already much higher than the entropy level of a normal drivinl) situation. If one compares actual entropy values with the longtime value Hn containing all data unti I this time, variations i n the drivinl) behaviour can be seen. The l arl)er the difference between the lonl)time entropy and the actua l values is the less

o ~-------------------------c--~--~~ o 2 4 6 8 10 12 14 16 18 t Imi n Fig. 4. Inductive information I x, n as a function of time

It is a problem to define an event to obtain an indication vector containing entropy Hn and informations I x,n . A definition must be given to avoid too great amounts of data which cannot be processed in time and with normal storage sizes. We decided to select the following events:

1. numbers of hea rt beats per minute which exceed the mean value of the minute be fOI-e . 2. I-evel-sal rate pet" minute of the steering wheel with a magni tude of more than ~ 0.5 degrees. 3. number of decreases of resistance per mi nute I"li th a vui at i on :.R ,. 2k ... Figure 5 shows the anal ogous signals with marke d events .

of a tired driver . This result can be im prov ed if further para meters li ke speed or optical informations are introduced. More investigations and the variation of selec tion and definition of events as wel I as va riation of the parameters . and z can further improve the results. First of all it seemed convenient t o collect experiences with the information deficit (compare Fig . 2. ) becau se more inf ormati on can be obtained in case of low information flow but also the part wit h an excess of in formation (see Fig. 2.) could be investiqated . An impleme ntation on a microprocessor system should re nder possible an on - line process wi thi n the car. Then a driver could actually be informed on - line about his driving style. That means an active contributio n to the safety of the system man - automobile. As it i s not poss i ble for a human being , to re cogn iz e the dri ving s t ate he actua ll y i s in a ca l cul ate d inf ormat i on ca n serve as a feedback to co r rect hi s drivi ng behaviour.

50 o

- 50 2 events

2 eve nts

Lo-----------------l~--------------~2~t / mi n

Fi g . 5. Analogous signals Furthel" classification can be done by se parating different amplitudes of al I parame ters and by crosscorrelation between the pa rametel-s . FOI"mer calculations \'Iere exclusi vely carried out with constant values ~ and z (, = 0 . 01, z = 3) . To distinguish bet\~een important events and I ess important ones the variation of t he learning parameter ~ and the variation of the number of diffe r ent events z can be taken into accoun t . CONCLUSION Though we measured only three parameters , the puls rate, the steering wheel reversal rate , and the skin resista nce , i t was possible to distinguish norma l sa f e dr i vin g behaviour from less safe behav i our i n case

ACKNOWL EDGEME NTS He l pfu l advice and encouragement gi ven by Prof . Dr. - Ing . W. Kumm are gratefully acknow l edged ; further it is a pl easure to acknowledge supportive interest and dis cussions gi ven by all my colleagues . REFERENC ES ADAC (without yr.) Untersuchung der

Unf~ l

le

auf Bundesautobahnen. ADAC - Zentrale, Munchen. Br~gas , P. (1974) . Verkehrsfunk und elek t ro nische Verkehrsle nkung . Techniken der Zukun ft, Heft 9. Bu sch , F. (1968). Verkehrss i cherung durch besond e r e straBenbau l iche und verkehrs 1enkende MaBnahmen. Schriftenrei he des Bundesministers fur Verkehr , Heft 33 . Carnap , R.

(1959). Induktive Logik und Wahr -

sch e inl i chke i t. 1. Auflage , Wien .

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A.S. Janitzki

Dilling, J. (1977 ) . Ergebnisse und Folgerungen aus dem Arbeitskreis 11 "Forschungen zum Verhalten im StraBenverkehr". Unfall- und Sicherheitsforschung StraBenverkehr Symposion 77, Heft 14, 363-364. Greenshields, B.O. (1963). Driver behaviour and related problems. Highway Research Record, ..?2, 14-32. GrUsser, O.-J. (1972). Informationstheorie und die Signalverarbeitung in den Sinnesorganen und im Nervensystem. Naturwissenschaften, ~, 436-447. Haftkopf, G. (1977). StraBengestaltung, Verkehrsablauf und Verkehrssicherheit. Unfall- und Sicherheitsforschung StraBenverkehr Symposion 77, Heft 14, 293- 303. Heinze, H. (1955). Die Eignung des psychogalvanischen Hautreflexes zur objektiven Erfassung eines ErmUdungsvorganges. Psychologische Beitrage, 98-113. Helander, M.G. (1975). Physiological reactions of drivers as indicators of road traffic demand. Transportation Research Record, 530, 1-17. Hoffmann, H. (1966). Das Kreislaufverhalten der KraftfahrzeugfUhrer wahrend der Fahrt. Habilitationsschrift Universitat Bonn, Medizinische Fakultat. HUK-Verband (1975). Innere Sicherheit im Auto, das Unfallgeschehen und seine Folgen. HUK-Verband der Haftpflicht-, Unfall- und Kraftverkehrsversicherer e.V., Hamburg. HUK-Verband (1977). Fakten zu Unfallgeschehen und Fahrzeugsicherheit. HUKVerband der Haftpflicht-, Unfall- und Kraftverkehrsversicherer e.V., MUnchen. Jani tzki, A. (1982). Ein Ansatz zur Reduzierung der Unfallquote infolge mens chlichen Versagens. StraBenverkehrstech~, to be published. Keidel, \'1.0. (1964). Kybernetische Leistungen des menschlichen Organismus. Elektrotechnische Zeitschrift, A 85, 769-777.

Kumm, W., and vi. vJoischnig (1970 ). Zur Entwicklung eines automatisch gesteuerten Verkeh rsUberwachung s- und Len ku ngssystems auf Schnellstra Ben. StraBenverkehrstech~, Heft 6, 203-208. Kumm, W. (1974). Nachrichtensysteme zur Verbesserung des individuellen SchnellstraBenverkehrs. Habilitationsschrift TH Aachen, Fakultat fUr Bauwesen. KUpfmUller, K. (1959 ). Informationsverarbeitung durch den Menschen. NTZ, ~, 68-74. Leva, I. (1913). Ober einige Begleiterscheinungen psychischer Vorgange mit besonderer BerUcksichtigung des psychogalvanischen Reflexphanomens. MUnch. med. Wschr., ll, 2386. Marko, H. (1965). Physikalische und biologische Grenzen der InformationsUbermittlung. Kybernetik, ~, 274-284. Michaels, R.M. (1960). Tension responses of drivers generated on urban streets. HRB Bull , ~, 29-43. Montagu, J.D. (1963). Habituation of the psychogalvanic reflex during serial tests. Journal of Psychosomatic Research, 2, 199-214. Overhof, C.-E. (1960). Ober das elektrische Verhalten spezieller Hautbezirke. Dissertation TH Karlsruhe, Fakultat fUr Maschinenwesen. Radl, G. W. (1976). Ingenieurpsychologische Forschungen hinsichtlich der StraBenverkehrssicherheit. TOV Rheinland, Koln. Sewell, R., and C. Perratt (1975). Data aquisition system for studies of driver performance in real traffic. Transportation Research Record, 530, 31-45. Spreng, M. (1976). Grenzen der sensorischen Informationsverarbeitung des Menschen. Naturwissenschaftliche Rundschau, 29, 11,377-386. Tafel, J., and W. Kumm (1978). Verkehrsbeeinflussung in einem rasterformig verdichteten Autobahnnetz (Land NordrheinWestfalen) Teil 11. Beitrage aus dem Gebiet der Informationstechnik. Forschungsberichte des Landes Nordrhein-

Di s tin gui s hing Sa f e fro m Les s Saf e Dri v in g

We s tfalen Nr. 2747 Fachgruppe Umwelt / Verkehr. Westdeutscher Verlag. Tej mar, J. (1979) . Ergonomische Analyse des Fehl verhaltens im Stra Benverkehr . Ver kehr smedizin, T020, 62-66. Venables, M. (196 7) , Skin resistance and skin potential . Manual of psycho-physiological methods. North Holland Publishing Comp., Amsterdam . Vogt , H. -P. (1977) . Die Vorstellungen des Gesetzgebers zur Fahrerausbildung. Unfall - und Si cherheitsforschung StraBenverkehr Symposion 77, Heft 14, 167-171.

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